The present invention relates to the field of natural language processing, and more specifically, text replacement utilizing tokenization from natural language processing.
Communication content consists of text, audio and even transformation of images to text by object recognition, expressed in a computer-readable format. This content is user-generated and consists of both professional and personal written works. Examples of communication content include websites, books, publications, and social media posts. Some communication content, such as social media posts, often contain metadata about the content to help provide not only content, but context. Metadata often includes information about location, engagement, and links shared. Communication content provides some insight on the content creator, as content parsed from the communication content can be utilized by a number of applications. For example, social media posts may be parsed to help identify appropriate targeted advertising.
Natural language processing is a field concerned with the interactions between computers and human (natural) languages. Tokenization is the process of utilizing natural language processing to break-up a stream of text into words, phrases, symbols, or other meaningful elements called tokens. Tokenization typically occurs at the word level and takes into consideration punctuation, spaces, contractions, hyphens, and emoticons. Tokens generated from content may become input for further processing.
Matching readers with appropriate books based on reader level is done in elementary schools and through online applications. Users can receive a reading level score based on reading comprehension tests. Software that examines a document's reading demand or difficulty level are also available to use by students and teachers.